Results 21 to 30 of about 149,758 (276)
Curve Registration of Functional Data for Approximate Bayesian Computation
Stats, 2021 Approximate Bayesian computation is a likelihood-free inference method which relies on comparing model realisations to observed data with informative distance measures.
Anthony Ebert +3 more
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Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data. [PDF]
PLoS ONE, 2017 Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell.
Zahra Narimani +4 more
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Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects
Entropy, 2022 Traditionally, Hawkes processes are used to model time-continuous point processes with history dependence. Here, we propose an extended model where the self-effects are of both excitatory and inhibitory types and follow a Gaussian Process.
Noa Malem-Shinitski +2 more
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Epidemics, 2023 Assessing the factors responsible for differences in outbreak severity for the same pathogen is a challenging task, since outbreak data are often incomplete and may vary in type across outbreaks (e.g., daily case counts, serology, cases per household ...
Alexander D. Meyer +5 more
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Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Hybrid BNs (HBNs) extend Bayesian networks (BNs) to both discrete and continuous variables. Among inference methods for HBNs, we focus on dynamic discretization (DD) that converts HBN to discrete BN for inference.
Yang Xiang, Hanwen Zheng
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Biases and Variability from Costly Bayesian Inference
Entropy, 2021 When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability.
Arthur Prat-Carrabin +3 more
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Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning
Entropy, 2021 It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction.
Chi-Ken Lu, Patrick Shafto
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Some Interesting Observations on the Free Energy Principle
Entropy, 2021 Biehl et al. (2021) present some interesting observations on an early formulation of the free energy principle. We use these observations to scaffold a discussion of the technical arguments that underwrite the free energy principle.
Karl J. Friston +2 more
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Semiparametric Regression Analysis via Infer.NET
Journal of Statistical Software, 2018 We provide several examples of Bayesian semiparametric regression analysis via the Infer.NET package for approximate deterministic inference in Bayesian models.
Jan Luts +3 more
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Exact Inference with Approximate Computation for Differentially Private Data via Perturbations
The Journal of Privacy and Confidentiality, 2022 This paper discusses how two classes of approximate computation algorithms can be adapted, in a modular fashion, to achieve exact statistical inference from differentially private data products.
Ruobin Gong
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